The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.
Manners of interconnections from concepts to concepts, from objects to objects, and from persons to persons, etc. are ubiquitous cross-domain relations. Cross-domain relations are, in many cases, natural things happening to people. For example, we are aware of the relationship between English texts and their French translation and vice versa. In addition, we select the jacket of suit to wear in style coherent to pants and shoes.
The computer′ problem of whether it can obtain a humanlike capacity to associate domains of two different kinds of images may be reconstructed with a conditional image generation problem. In other words, finding a mapping function from one domain to another is analogous to being responsive to given images in one domain for generating corresponding images in the other domain. Conventionally, such image-to-image translation has been studied mainly by methods in which a training set composed of mutually paired images is used for learning the mapping between the input images and the output images. However, it is very troublesome to prepare paired training data, and the data are unusable in many tasks. The recent introduction of Generative Adversarial Networks (GAN) has led active attempts to apply the algorithm to conditional image generation.
FIG. 1 is a schematic diagram of GAN. GAN was introduced by Non-Patent Document 1, as a relatively new machine learning architecture for a neural network. A machine learning algorithm, GAN belongs to a part of unsupervised learning, and it is a new type of generation model used to generate images. The GAN concept introduces a discriminator network (D), to solve the problem of training a generator network (G). The word “adversarial” refers to two adversarial networks, i.e., “discriminator” and “generator.” As illustrated in FIG. 1, the generator tries to generate a more realistic image that can fool the discriminator, while the discriminator continuously adjust parameters to discriminate the images generated by the generator from the real image. In terms of game theory, these networks compete with each other in a zero-sum game.